Multi_Agent_APF.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMuETiTXNg5Kguy4W5azbpo",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RiverTwilight/Awesome-Machine-Learning-Playground/blob/master/Multi_Agent_APF.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Overview\n",
        "\n",
        "The Artificial Potential Field (APF) algorithm is a method for robot path planning that is used to control the robot for avoiding obstacles in front of it\n",
        ". The APF algorithm considers the robot as a point in potential fields and then combines stretching toward the target and repulsion of obstacles. The final path of the output is the intended path"
      ],
      "metadata": {
        "id": "ij2RH_uiMVd_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Single Agent APF"
      ],
      "metadata": {
        "id": "bUeBFBnZMjNW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "from collections import namedtuple\n",
        "\n",
        "class Vector2D:\n",
        "    def __init__(self, x, y):\n",
        "        self.deltaX = x\n",
        "        self.deltaY = y\n",
        "        self.length = np.sqrt(x**2 + y**2)\n",
        "        self.direction = [0, 0] if self.length == 0 else [x/self.length, y/self.length]\n",
        "\n",
        "    def __add__(self, other):\n",
        "        return Vector2D(self.deltaX + other.deltaX, self.deltaY + other.deltaY)\n",
        "\n",
        "    def __sub__(self, other):\n",
        "        return Vector2D(self.deltaX - other.deltaX, self.deltaY - other.deltaY)\n",
        "\n",
        "    def __mul__(self, k):\n",
        "        return Vector2D(self.deltaX * k, self.deltaY * k)\n",
        "\n",
        "    def __div__(self, k):\n",
        "        return Vector2D(self.deltaX / k, self.deltaY / k)\n",
        "\n",
        "class Basic_APF:\n",
        "    def __init__(self, _start, _goal, _obstacles, _k_att, _k_rep, _rr, _step_size, _max_iters, _goal_threashold):\n",
        "        self.start = _start\n",
        "        self.goal = _goal\n",
        "        self.obstacles = _obstacles\n",
        "        self.k_att = _k_att\n",
        "        self.k_rep = _k_rep\n",
        "        self.rr = _rr\n",
        "        self.step_size = _step_size\n",
        "        self.max_iters = _max_iters\n",
        "        self.goal_threashold = _goal_threashold\n",
        "        self.current_pos = _start\n",
        "        self.iters = 0\n",
        "        self.is_path_plan_success = False\n",
        "        self.path = []\n",
        "\n",
        "    def attractive(self):\n",
        "        return (self.goal - self.current_pos) * self.k_att\n",
        "\n",
        "    def repulsion(self):\n",
        "        rep = Vector2D(0, 0)\n",
        "        for obs in self.obstacles:\n",
        "            t_vec = self.current_pos - obs\n",
        "            if t_vec.length <= self.rr:\n",
        "                rep = rep + (Vector2D(t_vec.direction[0], t_vec.direction[1]) * self.k_rep) * ((1.0 / t_vec.length - 1.0 / self.rr) / (t_vec.length**2))\n",
        "        return rep\n",
        "\n",
        "    def path_plan(self):\n",
        "        while self.iters < self.max_iters and (self.current_pos - self.goal).length > self.goal_threashold:\n",
        "            force_rep = self.repulsion()\n",
        "            force_att = self.attractive()\n",
        "            force_vec = force_rep + force_att\n",
        "            self.current_pos = self.current_pos + Vector2D(force_vec.direction[0], force_vec.direction[1]) * self.step_size\n",
        "            self.iters += 1\n",
        "            self.path.append(self.current_pos)\n",
        "        if (self.current_pos - self.goal).length <= self.goal_threashold:\n",
        "            self.is_path_plan_success = True\n",
        "        return self.is_path_plan_success\n",
        "\n",
        "class Improved_APF(Basic_APF):\n",
        "    def __init__(self, _start, _goal, _obstacles, _k_att, _k_rep, _rr, _step_size, _max_iters, _goal_threashold):\n",
        "        super().__init__(_start, _goal, _obstacles, _k_att, _k_rep, _rr, _step_size, _max_iters, _goal_threashold)\n",
        "\n",
        "    def repulsion(self):\n",
        "        rep = Vector2D(0, 0)\n",
        "        n = 2  # Exponent in the repulsion term\n",
        "        for obs in self.obstacles:\n",
        "            obs_to_rob = self.current_pos - obs\n",
        "            rob_to_goal = self.goal - self.current_pos\n",
        "            if obs_to_rob.length < self.rr:\n",
        "                rep_1_magnitude = self.k_rep * (1.0 / obs_to_rob.length - 1.0 / self.rr) / (obs_to_rob.length**2) * (rob_to_goal.length**n)\n",
        "                rep_1 = Vector2D(obs_to_rob.direction[0] * rep_1_magnitude, obs_to_rob.direction[1] * rep_1_magnitude)\n",
        "                rep_2_magnitude = self.k_rep * (n / 2) * ((1.0 / obs_to_rob.length - 1.0 / self.rr)**2) * (rob_to_goal.length**(n - 1))\n",
        "                rep_2 = Vector2D(rob_to_goal.direction[0] * rep_2_magnitude, rob_to_goal.direction[1] * rep_2_magnitude)\n",
        "                rep = rep + (rep_1 + rep_2)\n",
        "        return rep"
      ],
      "metadata": {
        "id": "MU18zHpODIRn"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Config\n",
        "\n",
        "k_att = 1.0\n",
        "k_rep = 0.8\n",
        "rr = 2\n",
        "step_size = 0.2\n",
        "max_iters = 100\n",
        "goal_threashold = 0.2\n",
        "step_size_ = 2\n",
        "start = Vector2D(0, 0)\n",
        "goal = Vector2D(18, 18)"
      ],
      "metadata": {
        "id": "ncgnPKRiQkQj"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# obstacles = [Vector2D(2, 3), Vector2D(6, 10), Vector2D(7, 9), Vector2D(4, 10), Vector2D(9, 7), Vector2D(12, 9), Vector2D(13, 12), Vector2D(14, 14)]\n",
        "\n",
        "def generate_random_vector(min_val, max_val, existing_vectors):\n",
        "    while True:\n",
        "        random_vector = Vector2D(random.randint(min_val, max_val), random.randint(min_val, max_val))\n",
        "        if random_vector not in existing_vectors:\n",
        "            return random_vector\n",
        "\n",
        "# define the constraints\n",
        "min_val = 0\n",
        "max_val = 20\n",
        "num_obstacles = 25\n",
        "\n",
        "# generate the random obstacles\n",
        "obstacles = []\n",
        "for _ in range(num_obstacles):\n",
        "    new_vector = generate_random_vector(min_val, max_val, obstacles)\n",
        "    obstacles.append(new_vector)\n",
        "\n",
        "print(obstacles)\n",
        "\n",
        "apf = Improved_APF(start, goal, obstacles, k_att, k_rep, rr, step_size, max_iters, goal_threashold)\n",
        "is_successed = apf.path_plan()\n",
        "print(is_successed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_xyQ-9lyM-Xk",
        "outputId": "f49c4ab6-9083-41d0-cad6-92d88e271930"
      },
      "execution_count": 250,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[<__main__.Vector2D object at 0x7fe41f438f70>, <__main__.Vector2D object at 0x7fe41f4381f0>, <__main__.Vector2D object at 0x7fe41f43bf40>, <__main__.Vector2D object at 0x7fe41f43acb0>, <__main__.Vector2D object at 0x7fe41f43a2c0>, <__main__.Vector2D object at 0x7fe41f438880>, <__main__.Vector2D object at 0x7fe41f43bb50>, <__main__.Vector2D object at 0x7fe41f4388e0>, <__main__.Vector2D object at 0x7fe41f439780>, <__main__.Vector2D object at 0x7fe41f439f00>, <__main__.Vector2D object at 0x7fe41f43a350>, <__main__.Vector2D object at 0x7fe41f43a110>, <__main__.Vector2D object at 0x7fe41f438400>, <__main__.Vector2D object at 0x7fe41f439c00>, <__main__.Vector2D object at 0x7fe41f43b970>, <__main__.Vector2D object at 0x7fe41f439bd0>, <__main__.Vector2D object at 0x7fe41f438a00>, <__main__.Vector2D object at 0x7fe41f439450>, <__main__.Vector2D object at 0x7fe41f4395d0>, <__main__.Vector2D object at 0x7fe41eddef80>, <__main__.Vector2D object at 0x7fe41eddcfa0>, <__main__.Vector2D object at 0x7fe41eddeb60>, <__main__.Vector2D object at 0x7fe41eddd330>, <__main__.Vector2D object at 0x7fe41eddf310>, <__main__.Vector2D object at 0x7fe41edde920>]\n",
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "planned_path = apf.path\n",
        "print(is_successed)\n",
        "plt.figure()\n",
        "plt.plot(start.deltaX, start.deltaY, 'go', label='Start')\n",
        "plt.plot(goal.deltaX, goal.deltaY, 'ro', label='Goal')\n",
        "for obs in obstacles:\n",
        "    plt.plot(obs.deltaX, obs.deltaY, 'ko', label='Obstacle')\n",
        "x_path = [pos.deltaX for pos in planned_path]\n",
        "y_path = [pos.deltaY for pos in planned_path]\n",
        "plt.plot(x_path, y_path, label='Planned path')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "5ltlDqyoM5pc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "69028ea1-b28c-46ba-b89e-fc26bbff45b7"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Multi-Agent APF"
      ],
      "metadata": {
        "id": "lLPa-OI5Q5t0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Multi_Agent_APF(Improved_APF):\n",
        "    def __init__(self, agents, _goal, _obstacles, _k_att, _k_rep, _rr, _step_size, _max_iters, _goal_threashold, _formation_offsets, _separation_distance):\n",
        "        super().__init__(agents[0], _goal, _obstacles, _k_att, _k_rep, _rr, _step_size, _max_iters, _goal_threashold)\n",
        "        self.agents = agents\n",
        "        self.agent_paths = [[] for _ in self.agents]\n",
        "        self.formation_offsets = _formation_offsets\n",
        "        self.separation_distance = _separation_distance\n",
        "\n",
        "    def separation(self):\n",
        "        # Separation force to maintain a safe distance between agents\n",
        "        separation_force = Vector2D(0, 0)\n",
        "        for agent in self.agents:\n",
        "            if agent != self.current_pos:  # Avoid calculating the force with itself\n",
        "                r = self.current_pos - agent\n",
        "                if r.length < self.separation_distance:  # Only consider agents within the separation distance\n",
        "                    direction_vector = Vector2D(r.direction[0], r.direction[1])\n",
        "                    # The separation force is inversely proportional to the distance\n",
        "                    separation_force = separation_force - direction_vector * (self.separation_distance / r.length)\n",
        "        return separation_force\n",
        "\n",
        "    def inter_agent_attraction(self, leader_pos, offset):\n",
        "        # Attractive force from the agent's target position to maintain formation\n",
        "        target_pos = leader_pos + offset\n",
        "        r = target_pos - self.current_pos\n",
        "        direction_vector = Vector2D(r.direction[0], r.direction[1])\n",
        "        return direction_vector * self.k_att * 0.1 * (r.length**2)\n",
        "\n",
        "    def path_plan(self):\n",
        "        for it in range(self.max_iters):\n",
        "            # Calculate the path for the leader (the first agent in the list)\n",
        "            self.current_pos = self.agents[0]\n",
        "            total_force = self.attractive() + self.repulsion()\n",
        "            self.current_pos = Vector2D(self.current_pos.deltaX, self.current_pos.deltaY) + Vector2D(total_force.direction[0], total_force.direction[1]) * self.step_size\n",
        "            self.agents[0] = self.current_pos  # Update the position of the leader\n",
        "            self.agent_paths[0].append(self.current_pos)  # Add the current position to the path of the leader\n",
        "\n",
        "            # For all other agents, just calculate the inter-agent attraction\n",
        "            for i, agent in enumerate(self.agents[1:], start=1):\n",
        "                self.current_pos = agent\n",
        "                total_force = self.inter_agent_attraction(self.agents[0], self.formation_offsets[i]) + self.repulsion() # Attraction to target position and repulsion from obstacles\n",
        "                self.current_pos = Vector2D(self.current_pos.deltaX, self.current_pos.deltaY) + Vector2D(total_force.direction[0], total_force.direction[1]) * self.step_size\n",
        "                self.agents[i] = self.current_pos\n",
        "                self.agent_paths[i].append(self.current_pos)\n",
        "\n",
        "            if self.current_pos.length < self.goal_threashold:\n",
        "                return True\n",
        "        return False"
      ],
      "metadata": {
        "id": "PRn40vetRxNz"
      },
      "execution_count": 246,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Config\n",
        "\n",
        "f_distance = 5\n",
        "k_att = 2.5\n",
        "k_rep = 0.8\n",
        "rr = 2\n",
        "step_size = 0.2\n",
        "max_iters = 500\n",
        "goal_threashold = 0.2\n",
        "step_size_ = 2\n",
        "start = Vector2D(0, 0)\n",
        "goal = Vector2D(20, 5)"
      ],
      "metadata": {
        "id": "jkftkhoGWdJw"
      },
      "execution_count": 247,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "agents = [Vector2D(0, -1), Vector2D(0, 0), Vector2D(1, -1), Vector2D(1, 0)]\n",
        "formation_offsets = [Vector2D(0, 0), Vector2D(0, -0.5), Vector2D(0.5, -0.5), Vector2D(0.5, 0)]\n",
        "apf = Multi_Agent_APF(agents, goal, obstacles, k_att, k_rep, rr, step_size, max_iters, goal_threashold, formation_offsets, 0)\n",
        "\n",
        "is_successed = apf.path_plan()\n",
        "print(is_successed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v0l-JrZiSuLF",
        "outputId": "57f31fda-665d-4905-8c5a-7f653d70f179"
      },
      "execution_count": 253,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.plot(goal.deltaX, goal.deltaY, 'ro', label='Goal')\n",
        "for i, obs in enumerate(obstacles):\n",
        "    if i == 0:\n",
        "        plt.plot(obs.deltaX, obs.deltaY, 'ko', label='Obstacle')\n",
        "    else:\n",
        "        plt.plot(obs.deltaX, obs.deltaY, 'ko')\n",
        "for i, agent_path in enumerate(apf.agent_paths):\n",
        "    x_path = [pos.deltaX for pos in agent_path]\n",
        "    y_path = [pos.deltaY for pos in agent_path]\n",
        "    plt.plot(x_path, y_path, label=f'Agent {i+1}')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "m3oA_eO1S8Pm",
        "outputId": "66af4e0e-8feb-4c9d-fdce-63b99d6817b9"
      },
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}